Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
git clone --depth 1 https://github.com/adonai-labs/agent-runwayWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/rules/adonai-labs/agent-runway/dotnet-clean-code)<a href="https://agentmods.dev/rules/adonai-labs/agent-runway/dotnet-clean-code"><img src="https://agentmods.dev/badge/rules/adonai-labs/agent-runway/dotnet-clean-code/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/rules/adonai-labs/agent-runway/dotnet-clean-code"><img src="https://agentmods.dev/badge/rules/adonai-labs/agent-runway/dotnet-clean-code.svg" alt="Reviewed on agentmods" width="80" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.01285 | $0.01285 |
| Opus 5.5 | $0.00514 | $0.00514 |
| Sonnet 5.5 | $0.00257 | $0.00257 |
| Haiku 4.5 | $0.00128 | $0.00128 |
Grade A, and why
dotnet-clean-code scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 165 lines — stays where its author put it; the contents beside it link to each section on GitHub.
.NET Clean Code Standards
C#-specific coding standards. For universal principles (naming intent, SRP, DRY, error handling), see engineering-principles.mdc.
Conflict resolution: Where this file provides C#-specific guidance that differs from
engineering-principles.mdc, this file takes precedence for.cs,.csproj, and.slnfiles. The universal rule provides the default; this rule provides the override.
C# naming conventions
- PascalCase: types, methods, properties, constants, public fields
- camelCase: parameters, local variables, private fields (with
_prefix for instance fields) Iprefix for interfaces:IOrderRepository, notOrderRepository- Async methods end in
Async:GetOrderAsync,SaveChangesAsync - Boolean properties:
IsActive,HasPermission,CanRetry - Event delegates end in
EventHandler; events use past tense:OrderPlaced,PaymentFailed
Dependency injection
Always inject via constructor; do not use new directly for services.
// Correct
public class OrderService(IOrderRepository repository, ILogger<OrderService> logger) { }
// Incorrect
public class OrderService
{
private readonly OrderRepository _repository = new OrderRepository();
}
- Use constructor injection; avoid property injection except in framework-controlled types
- Keep constructors lean; inject only what the class needs
- Do not call services inside constructors; defer to method bodies
Async/await correctness
// Correct: async all the way, CancellationToken passed through
public async Task<Order> GetOrderAsync(Guid id, CancellationToken ct)
=> await _repository.GetByIdAsync(id, ct);
// Incorrect: blocking async call — causes deadlocks
var order = GetOrderAsync(id).Result;
- Always
awaitasync calls; never use.Resultor.Wait() - Accept
CancellationTokenin all public async methods and pass it downstream - Never use
async voidexcept for event handlers; alwaysasync Task - Use
ConfigureAwait(false)in library code; not required in ASP.NET Core application code
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 165 lines · 1,285 tokens per session scan A c8189e989f99
dotnet-clean-code is a cursor rule published in the GitHub repository adonai-labs/agent-runway (2 stars, last pushed 17d ago), licensed MIT. It adds 1,285 tokens to every session, about $0.0051 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other cursor rules, from other repositories
optimize-dry-solid-principles-cursorrules-prompt-f
Cursor rules for optimization development with DRY and SOLID principles integration.
code-optimization
Guidelines for optimizing duplicate and poorly structured code.
language-agnostic-patterns
Language-agnostic programming patterns: SOLID, design patterns, clean code, and architecture. Load when refactoring, designing abstractions, or reviewing structure — not for everyday syntax.
code-review-en
Code Review Guidelines (English).
agent-master
Meta-orchestrator that classifies tasks and routes to the right combination of installed skills across caveman (output compression), superpowers (dev workflow), anthropic-skills (business/domain expertise), and wired tactical libraries (wshobson, davila7, impeccable, anthropic-official). 26 routing categories…
ai-dev-os-review
Performs a comprehensive self-review before creating a PR. Combines guideline compliance checking (L3) with design-level review (L2) and philosophical alignment (L1). Unlike @ai-dev-os-check which only checks rules, this also evaluates architecture decisions and code design quality.